We compare our model with both baselines as well as leading approaches for visual synthesis. Unconditional models include a baseline GAN [1]}, StyleGAN2 [2]}, Self-Attention GAN [3]}, the autoregressive VQGAN [4]}, the layerwise k-GAN [5]}, the two-stage SBGAN [6]} and the original GANformer. Conditional approaches include SBGAN, BicycleGAN [7]}, Pix2PixHD [8]}, and SPADE [9]}. We implement the unconditional methods within our public GANformer codebase and use the authors' official implementations for the conditional ones. All models have been trained with resolution of \(256 \times 256\)  and for an equal number of training steps, roughly spanning 10 days on a single V100 GPU per model. See section  for description of baselines and competing approaches, implementation details, hyperparameter settings, data preparations and training configuration.
